Automatic image tagging using two-layered Bayesian networks and mobile data from smart phones
Young-Seol Lee, Sung‐Bae Cho · 2012
As digital media technologies have improved, a large amount of media content has been produced. Tagging is an effective way to manage a great volume of multimedia content. However, manual tagging has limitations such as human fatigue and subjective and ambiguous keywords. In this paper, we present an automatic tagging method to generate semantic annotation on a mobile phone. In order to overcome the constraints of the mobile environment, the method uses two layered Bayesian networks. In contrast to existing techniques, this approach attempts to design probabilistic models with fixed tree structures and intermediate nodes. To evaluate the performance of this method, an experiment is conducted with data collected over a month. The result shows the effectiveness of our proposed method. Furthermore, a simple graphic user interface is developed to visualize and evaluate recognized activities and probabilities.